{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import Series, DataFrame, read_pickle\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from types import MethodType\n",
    "\n",
    "\n",
    "def select_time(s, start_time, end_time):\n",
    "    if start_time == 'default':\n",
    "        start_timestamp = s.index[0]\n",
    "    else:\n",
    "        try:\n",
    "            start_timestamp = time.mktime(\n",
    "                time.strptime(start_time, \"%Y-%m-%d %H:%M\"))\n",
    "        except ValueError:\n",
    "            print(\n",
    "                '！！起始日期格式错误！！，应为 \\'YYYY-mm-dd MM:HH\\' 如 \\'2020-05-05 15:05\\'\\n**已默认从第一条数据开始**\\n')\n",
    "            start_timestamp = s.index[0]\n",
    "    # 处理start_time参数\n",
    "    if end_time == 'default':\n",
    "        end_timestamp = s.index[-1]\n",
    "    else:\n",
    "        try:\n",
    "            end_timestamp = time.mktime(\n",
    "                time.strptime(end_time, \"%Y-%m-%d %H:%M\"))\n",
    "        except ValueError:\n",
    "            print(\n",
    "                '！！截止日期格式错误！！，应为 \\'YYYY-mm-dd MM:HH\\' 如\\'2020-05-05 15:05\\'\\n**已默认最后一条数据截止**\\n')\n",
    "            end_timestamp = s.index[-1]\n",
    "    # 对series进行切片（选定的时间段）\n",
    "    return s[(s.index >= start_timestamp) & (s.index <= end_timestamp)]\n",
    "\n",
    "\n",
    "def prep_plt(kinds):\n",
    "    kinds_len = len(kinds)\n",
    "    if kinds_len == 1:\n",
    "        sub_row = 1\n",
    "        sub_col = 1\n",
    "        fig = plt.figure(figsize=(12, 6), facecolor='w')\n",
    "    elif kinds_len == 2:\n",
    "        sub_row = 1\n",
    "        sub_col = 2\n",
    "        fig = plt.figure(figsize=(18, 6), facecolor='w')\n",
    "    elif (kinds_len == 3 or kinds_len == 4):\n",
    "        sub_row = 2\n",
    "        sub_col = 2\n",
    "        fig = plt.figure(figsize=(18, 12), facecolor='w')\n",
    "    elif (kinds_len == 5 or kinds_len == 6):\n",
    "        sub_row = 2\n",
    "        sub_col = 3\n",
    "        fig = plt.figure(figsize=(24, 16), facecolor='w')\n",
    "    elif kinds_len == 7:\n",
    "        sub_row = 3\n",
    "        sub_col = 3\n",
    "        fig = plt.figure(figsize=(24, 24), facecolor='w')\n",
    "    else:\n",
    "        print('error')\n",
    "\n",
    "    return sub_row, sub_col, fig\n",
    "\n",
    "\n",
    "def sort_by(s, k, av):\n",
    "    ls = []\n",
    "    for i in s.index:\n",
    "        d = dict(time=i)\n",
    "        for j in range(len(s[i])):\n",
    "            # display(s[i])\n",
    "            row_av = s[i].iloc[j, 0]\n",
    "            row_data = int(s[i].iloc[j][k])\n",
    "            d.update({row_av: row_data})\n",
    "        # print(d)\n",
    "        ls.append(d)\n",
    "    # print(ls)\n",
    "    df = DataFrame(ls)\n",
    "    df.set_index(['time'], inplace=True)\n",
    "    if av != 'all':\n",
    "        df = df[av]\n",
    "    return df\n",
    "\n",
    "\n",
    "def plt_line(df, title, fig, sub_row, sub_col, t):\n",
    "    ax = fig.add_subplot(sub_row, sub_col, t)\n",
    "    df.plot(ax=ax)\n",
    "    ax.grid()\n",
    "    strtime = []\n",
    "    for i in ax.get_xticks():\n",
    "        strtime.append(time.strftime(\"%m/%d\\n%H:%M\", time.localtime(i)))\n",
    "    ax.set_xticklabels(strtime)\n",
    "    ax.set_title(title)\n",
    "\n",
    "\n",
    "def plt_bar(df, title, fig, sub_row, sub_col, t):\n",
    "    ax = fig.add_subplot(sub_row, sub_col, t)\n",
    "    df.plot(kind='bar', ax=ax)\n",
    "    ax.grid()\n",
    "    #strtime = []\n",
    "    # for i in ax.get_xticks():\n",
    "    #    strtime.append(time.strftime(\"%m/%d\\n%H:%M\",time.localtime(i)))\n",
    "    # ax.set_xticklabels(strtime)\n",
    "    ax.set_title(title)\n",
    "\n",
    "\n",
    "def get_interval(df, interval):\n",
    "    interval_df = DataFrame()\n",
    "    first = df.index[0]\n",
    "    last = df.index[-1]\n",
    "    start = first\n",
    "    end = start\n",
    "    while(end <= last):\n",
    "        end = start + interval\n",
    "        if df[(df.index >= start) & (df.index <= end)].empty:  # 循环的最后一次有可能切片为空\n",
    "            break\n",
    "        t_df = df[(df.index >= start) & (df.index <= end)]\n",
    "        # print(t_df.index)\n",
    "        time_str = time.strftime(\n",
    "            \"%m/%d\\n%H:%M\", time.localtime(t_df.index[-1]))\n",
    "        interval_df[time_str] = t_df.iloc[-1]-t_df.iloc[0]\n",
    "        start = end\n",
    "    return interval_df.T\n",
    "    # return t_df.iloc[-1]-t_df.iloc[0]\n",
    "    # return t_df\n",
    "\n",
    "\n",
    "def av_title():\n",
    "    s = read_pickle('time_series.pkl')\n",
    "    title = list(s[s.index[-1]]['title'])\n",
    "    av_ls = list(s[s.index[-1]]['AV'])\n",
    "    print('av号与标题对照如下：')\n",
    "    for i in range(len(av_ls)):\n",
    "        print(str(av_ls[i]) + '\\t' + str(title[i]))\n",
    "\n",
    "\n",
    "def accumulate(kinds='all', start_time='default', end_time='default', av='all'):\n",
    "    # 处理默认kinds参数\n",
    "    if kinds == 'all':\n",
    "        kinds = ['view', 'like', 'coin', 'favourite', 'danmu', 'share', 'reply']\n",
    "    # 读取爬虫所保存的数据\n",
    "    s = read_pickle('time_series.pkl')\n",
    "    # 在series的时候对时间进行选取更为方便\n",
    "    s = select_time(s, start_time, end_time)\n",
    "    # 为fig做准备\n",
    "    sub_row, sub_col, fig = prep_plt(kinds)\n",
    "    # 循环画多个子图\n",
    "    for i in range(len(kinds)):\n",
    "        # 将series转换为dataframe\n",
    "        df = sort_by(s, kinds[i], av)\n",
    "        # 画子图\n",
    "        plt_line(df, kinds[i], fig, sub_row, sub_col, i+1)\n",
    "    fig.show()\n",
    "\n",
    "\n",
    "def increase(kinds='all', start_time='default', end_time='default', av='all', interval=86400):\n",
    "    # 处理默认kinds参数\n",
    "    if kinds == 'all':\n",
    "        kinds = ['view', 'like', 'coin', 'favourite', 'danmu', 'share', 'reply']\n",
    "    # 读取爬虫所保存的数据\n",
    "    s = read_pickle('time_series.pkl')\n",
    "    # 在series的时候对时间进行选取更为方便\n",
    "    s = select_time(s, start_time, end_time)\n",
    "    # 为fig做准备\n",
    "    sub_row, sub_col, fig = prep_plt(kinds)\n",
    "    # 循环画多个kinds子图\n",
    "    for i in range(len(kinds)):\n",
    "        # 将series转换为dataframe\n",
    "        df = sort_by(s, kinds[i], av)\n",
    "        df_increase = get_interval(df, interval)\n",
    "        # 画子图\n",
    "        plt_bar(df_increase, kinds[i], fig, sub_row, sub_col, i+1)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "av号与标题对照如下：\n",
      "200633855\t【kaleidoscope】学校的创意影像作业如何拿到“几乎满分”——创意短片幕后\n",
      "840501089\t【kaleidoscope】同名创意短片\n",
      "540421569\t【kaleidoscope】你真的应该学python吗？学python前必看！\n"
     ]
    }
   ],
   "source": [
    "av_title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画增长量柱状图\n",
    "increase(kinds=['view'], av=[540421569, 840501089, 200633855], start_time='2020-5-10 12:00', end_time='2020-6-10 12:00', interval=169200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画累计折线图\n",
    "accumulate(kinds=['view', 'coin'], av=[540421569, 840501089], start_time='2020-5-10 12:00', end_time='2020-6-10 12:00')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
